https://towardsdatascience.com/geometric-foundations-of-deep-learning-94cdd45b451d?gi=d7fa7307aa63 Geometric foundations of Deep LearningGeometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principles
What I Read: Difficulty of Graph Anonymisation
https://www.timlrx.com/blog/tracetogether-and-the-difficulty-of-graph-anonymisation Timothy Lin@timlrxxSunday, February 7, 2021TraceTogether and the Difficulty of Graph Anonymisation “The word “anonymised data” seems to convey a certain sense of certainty that user information cannot be back-derived.
What I Read: HuggingFace Transformers
https://medium.com/georgian-impact-blog/how-to-incorporate-tabular-data-with-huggingface-transformers-b70ac45fcfb4 How to Incorporate Tabular Data with HuggingFace TransformersGeorgianOct 23 “At Georgian, we find ourselves working with supporting tabular feature information as well as unstructured text data. We found that
What I Read: Attention with Performers
https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Rethinking Attention with PerformersFriday, October 23, 2020Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research “To resolve these issues, we introduce the Performer, a Transformer architecture with
What I Read: automatic differentiation with graphs
https://ai.facebook.com/blog/a-new-open-source-framework-for-automatic-differentiation-with-graphs A new open source framework for automatic differentiation with graphsOctober 8th, 2020 “Just as PyTorch provides a framework for automatic differentiation with tensors, GTN provides such a framework for